Research Paper#Image Restoration, Diffusion Models, Film Restoration🔬 ResearchAnalyzed: Jan 3, 2026 06:19
HaineiFRDM: Diffusion Model for Film Defect Restoration
Published:Dec 31, 2025 16:18
•1 min read
•ArXiv
Analysis
This paper addresses the limitations of existing open-source film restoration methods, particularly their reliance on low-quality data and noisy optical flows, and their inability to handle high-resolution films. The authors propose HaineiFRDM, a diffusion model-based framework, to overcome these challenges. The use of a patch-wise strategy, position-aware modules, and a global-local frequency module are key innovations. The creation of a new dataset with real and synthetic data further strengthens the contribution. The paper's significance lies in its potential to improve open-source film restoration and enable the restoration of high-resolution films, making it relevant to film preservation and potentially other image restoration tasks.
Key Takeaways
- •Proposes HaineiFRDM, a diffusion model-based framework for film restoration.
- •Employs patch-wise training and testing for high-resolution film restoration.
- •Introduces position-aware modules and a global-local frequency module.
- •Constructs a new film restoration dataset with real and synthetic data.
- •Demonstrates superior performance compared to existing open-source methods.
Reference
“The paper demonstrates the superiority of HaineiFRDM in defect restoration ability over existing open-source methods.”